Multi-objective Optimal Design Of Heat Exchangers Based On Genetic Algorithm

Tube-bundle heat exchangers are essential equipments in the power plants. Both the super heaters and the reheaters are type of cross-flow tube bundle heat exchangers. In this paper, we take the last stage super heater which is a stagger arranged tube bundle heat exchanger as our research model.few references have investigated The optimal design approach of tube-bundle heat exchangers However, the optimization of heat exchangers based on the combination of the first and second law of thermodynamics has been increasingly popular in recent decades as the concept of entropy generation minimization has been proposed by Bejan. The design of heat exchangers should be adapted to their applications; otherwise their performances will be deceiving and their costs excessive. To meet the minimization of cost and the minimization of energy losses which cannot be reached at the same time, Since mentioned objectives are conflicting, no single solution can well-satisfy both objective functions simultaneously. In other words, any attempt to increase the value of the total rate of heat transfer leads to the higher total cost of the system which is certainly undesirable, Therefore, multi-objective optimization using genetic algorithm are applied extensively in recent years. The purpose of our paper is to get the minimum of pressure drop of each side and the least cost while the entropy generation minimization is obtained at the same time. Seven objective functions are optimized such as cost and pressure drop.6 structure sizes including tube side outer diameter, lateral tube pitch,number of tube pass, number of tube pass, number of tube in one pass, tube length were selected as decision variables The main advantage of this work is providing a set of optimal solutions each of which can be selected by the designer based on the project's limits and the available investment. The paper also compared the optimization results with the design data of the current power plant,the results show that,the selected optimal alternatives are superior to the designed one in terms of all the objectives.